an AI meal planner for home cooks
Estimated monthly cost at the usage scale below — billed by actual usage
2.40 req/s
Peak Concurrency
280.38 MB
Database Storage
0 GB
Object Storage
0 GB
Outbound data transfer
What is this app
AI Meal Planner helps people in the US who cook at home create personalized three-day meal plans. Users enter body statistics, activity level, goals, food preferences, and restrictions, and the app generates nine meals covering breakfast, lunch, and dinner. Calories and macronutrients are retrieved from the USDA food nutrition database, while the app also creates a gram-based shopping list that reuses core ingredients across the plan.
As a home cook, I want to enter my body statistics, activity level, goal, food preferences, and restrictions so that the app can tailor my meal plan.
As a home cook, I want to generate a three-day meal plan with breakfast, lunch, and dinner so that I know what to cook.
As a home cook, I want to view calories and macros for every planned meal so that I can understand its nutrition.
As a home cook, I want to view an aggregated shopping list in grams so that I can buy shared ingredients without unnecessary leftovers.
As a home cook, I want to review my previously generated meal plans so that I can revisit earlier plans.
As an anonymous visitor, I want to view an overview of the meal-planning app so that I can understand its purpose before registering.
50000
home_cooks
Registered people in the US who enter personal nutrition inputs, generate meal plans, review USDA-backed nutrition values, and use consolidated shopping lists.
0
anonymous_visitors
Unregistered visitors who can view the public product overview but cannot save nutrition profiles or generate meal plans.
Requirements to minimum viable plan to cost
Momen doesn't give a vague quote: it first sizes the project's actual demand for each capability and resource, then costs each item out.
The numbers in the "Project demand" column below — their scale assumptions and derivations — are detailed in Scale & sizing.
Minimum viable plan
BASIC
Multiple workflows and external production branding require at least BASIC.
See the capability-by-capability assessment ▾
PROJECT DEMAND
ALLOWANCE PROVIDED (COMPOSITION)
Basic
Basic is the minimum viable plan
Basic
(Plan tier: BASIC)
See minimum viable plan above
39
single-tenant kit
3 req/s
not purchased
(plan/kit 5 req/s)
Covered by plan/kit (margin ~67%)
0
Database storage add-on
986.81 MB
10GB × 1/year, amortized monthly
(plan/kit 200.00 MB + add-on +786.81 MB)
Add-on fills +786.81 MB gap beyond plan/kit
8.33
Object storage
add-on
0 B
not purchased
(plan/kit 2.00 GB)
No requirement
0
Outbound data transfer add-on
0 B/mo
not purchased
(plan/kit 2.00 GB/mo)
No requirement
0
AI points add-on
97.9M points
6,000,000 AI Points × 17/month
(plan/kit 103.0M points + add-on +96.9M points)
Add-on fills +96.9M points gap beyond plan/kit
170
plan + add-ons · usage-based · no development cost
Your options for this project
In the table below, the "monthly infrastructure" for the self-built / AI routes is derived from the AWS list prices shown below, sized against this project's actual usage.
2 × t4g.large × 730h
EC2 t4g.large → 98.11
2 × db.m6g.large (Multi-AZ) × 730h
RDS PostgreSQL db.m6g.large → 232.14
0.29 GB-month
RDS gp3 storage → 0.03
0.00 GB-month
S3 Standard → 0
0.00 GB (first 100GB free)
Internet egress → 0
Total ≈ $330.29 / mo
Pure cloud resources
DEVELOPMENT COST
MONTHLY CLOUD INFRA ($/MO)
MONTHLY TOTAL
(INFRA + OPS)
Traditional outsourcing / build in-house
about $95,000 - $150,000
≈ $330
Based on the AWS estimate above
≈ $2,330
Infra $330 + ops ~$2,000
4-6 months
5 / 5
Vibe Coding
about $14,000 - $22,000
≈ $330
Based on the AWS estimate above
≈ $1,830
Infra $330 + ops ~$1,500
hours
5 / 5
AI full-stack generation
about $6,000 - $10,000
≈ $330
Based on the AWS estimate above
≈ $1,830
Infra $330 + ops ~$1,500
hours
3 / 5
Off-the-shelf SaaS / vertical solution
$0
N/A
Priced per seat, not by cloud infra
≈ $16,000+
Per-seat pricing, tens of thousands of users
days
1 / 5
Momen
Recommended
$0
$47
All-in: egress / storage / auto-scaling; excludes AI usage
$47
hours
5 / 5
Servers, databases, traffic and storage are inherent infrastructure costs for this project — you pay them whether you outsource, use Cursor or Lovable, or build it yourself (self-built runs ≈ $441/mo at AWS list prices, often more), plus the extra dev and ops staff. Momen bundles all of it into $368/mo all-in and removes the need for an ops team.
The frontend can be generated with AI tools (Cursor, Lovable, etc.); the hard part is the backend — auth, database, scaling, data security and ops. Momen delivers a production-grade backend as a BaaS: keep the vibe-coding speed on the frontend, while the backend runs on proven infrastructure — reliable, with no self-hosting or ops.
What scale this estimate assumes, and how the numbers are derived
Cost depends heavily on usage volume. First see the key assumptions and business scenarios this estimate uses, then the full calculation derived from each scenario for every resource — all adjustable to your real situation.
We assume 50000 registered accounts because the description gives no explicit user count or directly implied customer scale. This is the standard planning assumption for a broadly applicable consumer meal-planning product and can be adjusted when expected US adoption or launch commitments are known.
We assume 225 equivalent running days because this is a standard consumer SaaS product expected to grow progressively rather than launch with a full pre-existing dataset or experience a sharply concentrated burst. If the app launches with a large imported user base or a major short-term campaign, this parameter can be adjusted.
Evening Meal Planning Routine
Evening Meal Planning Routine
Evening Meal Planning Routine
30
times/month
2.4 req/s
Peak load
We assume the evening is the principal recurring time window because home cooks commonly review nutrition information, generate plans, and prepare shopping lists after work. The period is not driven by an external synchronized signal, so its duration represents a broad 1,800-second routine window rather than a narrow burst. The purposes are separated into plan generation, profile maintenance, saved-plan review, and grocery review because they create different read and write patterns and have different core actions. The period occurs daily during stable operation, so its monthly frequency is estimated at 30 occurrences.
Main impact: Peak Concurrency
The scenarios above set the assumptions for each resource; below is the full calculation based on those assumptions. Click to expand each item.
How this app works
What exactly does this budget support? Broken down by business scenario, showing the pages, data tables, automation flows and AI
assistant behind each one.
Public browsing flow for learning what the meal-planning app does.
App Overview Page
Create or update the personal inputs used for meal generation.
App Overview Page
users
Primary end-to-end flow for producing and reviewing a three-day meal plan.
App Overview Page
users
meal_plan_builder
generate_meal_plan
Review the meal details and the consolidated grocery requirements.
App Overview Page
users
Browse and reopen previously generated meal plans.
App Overview Page
users
page
data_table
ai_agent
action_flow
App Overview Page
Public introduction to the meal-planning product and its core value.
Nutrition Profile Page
Form for entering and maintaining the personal nutrition inputs used for meal planning.
Meal Plan Page
Generation and detailed review of a user's three-day meal plan and its USDA-backed nutrition values.
Shopping List Page
Aggregated grocery list showing the total grams needed for the selected plan.
Plan History Page
List and detail access for a user's previously generated meal plans.
What you might want to know about this project
The Q&A below is generated by AI based on this project's type, features and scale.
What will drive this app's resource usage?
Are the nutrition values generated by AI?
What does one meal plan contain?
Does the app require an external food database?
Which user inputs affect plan generation?
Copy to discuss with AI
Challenge & re-estimate